Unsupervised learning of individuals and categories from images

Neural Computation
Stephen Waydo, Christof Koch

Abstract

Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data.

References

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Oct 6, 2006·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Stephen WaydoChristof Koch
Jan 17, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Thomas SerreTomaso Poggio
Apr 4, 2007·Proceedings of the National Academy of Sciences of the United States of America·Thomas SerreTomaso Poggio

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Citations

Feb 1, 2013·PloS One·Christopher Kanan
Jan 7, 2014·PloS One·Xiaolin HuBo Zhang
Feb 12, 2008·Trends in Cognitive Sciences·R Quian QuirogaI Fried
Aug 29, 2014·Journal of Anatomy·Hernan G ReyRodrigo Quian Quiroga
Jun 10, 2015·Consciousness and Cognition·Anna AnzulewiczAxel Cleeremans
Oct 24, 2008·Network : Computation in Neural Systems·Zacharias AndroulidakisMalcolm W Brown
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
Nov 30, 2016·Frontiers in Systems Neuroscience·Kun XieJoe Z Tsien
May 14, 2020·Scientific Reports·Carlos Calvo TapiaValeri A Makarov
Dec 1, 2020·Neural Computation·Dequan JinPenghe Chen
Nov 8, 2008·Current Opinion in Neurology

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